San Francisco Housing Cost Analysis

In this assignment, you will perform fundamental analysis for the San Francisco housing market to allow potential real estate investors to choose rental investment properties.

# imports
import panel as pn
pn.extension('plotly')
import plotly.express as px
import pandas as pd
import hvplot.pandas
import matplotlib.pyplot as plt
import numpy as np
import os
from pathlib import Path
from dotenv import load_dotenv

import warnings
warnings.filterwarnings('ignore')

# Read the Mapbox API key
load_dotenv()
map_box_api = os.getenv("MAP_BOX_API")

Load Data

# Read the census data into a Pandas DataFrame
file_path = Path("Data/sfo_neighborhoods_census_data.csv")
sfo_data = pd.read_csv(file_path, index_col="year")
sfo_data.head()

	neighborhood	sale_price_sqr_foot	housing_units	gross_rent
year
2010	Alamo Square	291.182945	372560	1239
2010	Anza Vista	267.932583	372560	1239
2010	Bayview	170.098665	372560	1239
2010	Buena Vista Park	347.394919	372560	1239
2010	Central Richmond	319.027623	372560	1239

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Housing Units Per Year

In this section, you will calculate the number of housing units per year and visualize the results as a bar chart using the Pandas plot function.

Hint: Use the Pandas groupby function.

Optional challenge: Use the min, max, and std to scale the y limits of the chart.

# Calculate the mean number of housing units per year (hint: use groupby) 
df_info = pd.DataFrame(sfo_data.groupby(sfo_data.index).housing_units.mean())
df_info

	housing_units
year
2010	372560
2011	374507
2012	376454
2013	378401
2014	380348
2015	382295
2016	384242

# Save the dataframe as a csv file
df_info.to_csv('housing_units_by_year.csv')

# Use the Pandas plot function to plot the average housing units per year.
# Note: You will need to manually adjust the y limit of the chart using the min and max values from above.
df_info.plot.bar(ylim = (df_info.housing_units.min() - df_info.housing_units.std(), df_info.housing_units.max() + df_info.housing_units.std()))

# Optional Challenge: Use the min, max, and std to scale the y limits of the chart
# Please note that I already did this in the code above.

<matplotlib.axes._subplots.AxesSubplot at 0x7f09da446760>

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Average Housing Costs in San Francisco Per Year

In this section, you will calculate the average monthly rent and the average price per square foot for each year. An investor may wish to better understand the sales price of the rental property over time. For example, a customer will want to know if they should expect an increase or decrease in the property value over time so they can determine how long to hold the rental property. Plot the results as two line charts.

Optional challenge: Plot each line chart in a different color.

# Calculate the average sale price per square foot and average gross rent
avgs = sfo_data.groupby(sfo_data.index)[['sale_price_sqr_foot','gross_rent']].mean()
avgs

	sale_price_sqr_foot	gross_rent
year
2010	369.344353	1239
2011	341.903429	1530
2012	399.389968	2324
2013	483.600304	2971
2014	556.277273	3528
2015	632.540352	3739
2016	697.643709	4390

# Create two line charts, one to plot the average sale price per square foot and another for average montly rent

# Line chart for average sale price per square foot
line1 = avgs.sale_price_sqr_foot.plot(c = 'purple')
title = plt.title('Average Price Per SqFt')
ylabel = plt.ylabel('Price Per SqFt')
plt.grid = True
plt.show()

# Line chart for average montly rent
line2 = avgs.gross_rent.plot(c = 'red')
title = plt.title('Average Gross Rent by Year')
ylabel = plt.ylabel('Price Per SqFt')
plt.show()

---

Average Prices by Neighborhood

In this section, you will use hvplot to create two interactive visulizations of average prices with a dropdown selector for the neighborhood. The first visualization will be a line plot showing the trend of average price per square foot over time for each neighborhood. The second will be a line plot showing the trend of average montly rent over time for each neighborhood.

Hint: It will be easier to create a new DataFrame from grouping the data and calculating the mean prices for each year and neighborhood

# Group by year and neighborhood and then create a new dataframe of the mean values
data = sfo_data[['neighborhood','sale_price_sqr_foot']].groupby([sfo_data.index, 'neighborhood']).mean().reset_index().dropna().sort_values(by ='year')
data

	year	neighborhood	sale_price_sqr_foot
0	2010	Alamo Square	291.182945
32	2010	Mission Bay	489.805378
33	2010	Mission Dolores	257.987999
34	2010	Mission Terrace	262.876026
35	2010	Nob Hill	307.677477
...	...	...	...
369	2016	Inner Richmond	403.311132
370	2016	Inner Sunset	445.923828
371	2016	Jordan Park/Laurel Heights	704.566419
361	2016	Excelsior	534.226250
396	2016	Westwood Park	631.195426

392 rows × 3 columns

HVPLOT Dropdown

# Use hvplot to create an interactive line chart of the average price per sq ft.
# The plot should have a dropdown selector for the neighborhood
hvplot1 = data.hvplot.line(x = 'year', y = 'sale_price_sqr_foot', groupby ='neighborhood', hover_cols = 'all')
hvplot1

# Use hvplot to create an interactive line chart of the average monthly rent.
# The plot should have a dropdown selector for the neighborhood
data1 = pd.DataFrame(sfo_data[['neighborhood','gross_rent']].groupby([sfo_data.index, 'neighborhood']).mean().reset_index().dropna().sort_values(by ='year'))
hvplot2 = data1.hvplot.line(x = 'year', y = 'gross_rent', groupby ='neighborhood')
hvplot2

The Top 10 Most Expensive Neighborhoods

In this section, you will need to calculate the mean sale price per square foot for each neighborhood and then sort the values to obtain the top 10 most expensive neighborhoods on average. Plot the results as a bar chart.

# Getting the data from the top 10 expensive neighborhoods to own
expensive = pd.DataFrame(sfo_data.groupby(['neighborhood']).sale_price_sqr_foot.mean().sort_values(ascending = False).head(10).reset_index())
expensive

	neighborhood	sale_price_sqr_foot
0	Union Square District	903.993258
1	Merced Heights	788.844818
2	Miraloma Park	779.810842
3	Pacific Heights	689.555817
4	Westwood Park	687.087575
5	Telegraph Hill	676.506578
6	Presidio Heights	675.350212
7	Cow Hollow	665.964042
8	Potrero Hill	662.013613
9	South Beach	650.124479

# Plotting the data from the top 10 expensive neighborhoods
expensive_plot = expensive.hvplot.bar(figsize = (30,20), x = 'neighborhood', rot = 90)
expensive_plot

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Comparing cost to purchase versus rental income

In this section, you will use hvplot to create an interactive visualization with a dropdown selector for the neighborhood. This visualization will feature a side-by-side comparison of average price per square foot versus average montly rent by year.

Hint: Use the hvplot parameter, groupby, to create a dropdown selector for the neighborhood.

# Fetch the previously generated DataFrame that was grouped by year and neighborhood
a = sfo_data.groupby(['year','neighborhood']).mean().drop(columns = 'housing_units')
a = a.reset_index()
plot1 = a.hvplot.bar(groupby = 'neighborhood', rot = 90, x = 'year', figsize = (30,10))
plot1

# Plotting the data from the top 10 expensive neighborhoods
expensive_plot = expensive.hvplot.bar(figsize = (20,10))
expensive_plot

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Neighborhood Map

In this section, you will read in neighborhoods location data and build an interactive map with the average house value per neighborhood. Use a scatter_mapbox from Plotly express to create the visualization. Remember, you will need your Mapbox API key for this.

Load Location Data

# Load neighborhoods coordinates data
coordinates = pd.read_csv('Data/neighborhoods_coordinates.csv')
coordinates.columns = ['neighborhood','lat','lon']
coordinates = pd.DataFrame(coordinates)

mean_df = pd.DataFrame(sfo_data.groupby(['neighborhood']).mean())
mean_df.head()

	sale_price_sqr_foot	housing_units	gross_rent
neighborhood
Alamo Square	366.020712	378401.0	2817.285714
Anza Vista	373.382198	379050.0	3031.833333
Bayview	204.588623	376454.0	2318.400000
Bayview Heights	590.792839	382295.0	3739.000000
Bernal Heights	576.746488	379374.5	3080.333333

Data Preparation

You will need to join the location data with the mean values per neighborhood.

1. Calculate the mean values for each neighborhood.

2. Join the average values with the neighborhood locations.

# At this point the data is merged and all mean values are calculated.
new_df = pd.DataFrame(pd.merge(coordinates,mean_df, how = 'left', on = ['neighborhood','neighborhood']).dropna())
new_df.head()

	neighborhood	lat	lon	sale_price_sqr_foot	housing_units	gross_rent
0	Alamo Square	37.791012	-122.402100	366.020712	378401.0	2817.285714
1	Anza Vista	37.779598	-122.443451	373.382198	379050.0	3031.833333
2	Bayview	37.734670	-122.401060	204.588623	376454.0	2318.400000
3	Bayview Heights	37.728740	-122.410980	590.792839	382295.0	3739.000000
5	Buena Vista Park	37.768160	-122.439330	452.680591	378076.5	2698.833333

# Calculate the mean values for each neighborhood
# Already done above.

# Join the average values with the neighborhood locations
# Already done above

Mapbox Visualization

Plot the average values per neighborhood using a Plotly express scatter_mapbox visualization.

# So I just had to redo my mapbox after the message below popped up. Once it shows up it completely takes over your code and you can't change anything about it. Also I can't seem to get rid of it. So it will just stay here.

# Set the mapbox access token

load_dotenv()

mapbox_api = os.getenv('MAP_BOX_API')

# Create a scatter mapbox to analyze neighborhood info
map_box = px.scatter_mapbox(
    new_df,
    lat="lat",
    lon="lon",
    size="sale_price_sqr_foot",
    color="neighborhood",
    zoom=10
)
map_box.update_layout(
    mapbox_style="dark")
map_box.show() #display map

map_box = px.scatter_mapbox(
    new_df,
    lat="lat",
    lon="lon",
    color="neighborhood",
    zoom=10
)
map_box.update_layout(
    mapbox_style="carto-positron")
map_box.show() # Got this almost directly from one of the activities.

Cost Analysis - Optional Challenge

In this section, you will use Plotly express to create visualizations that investors can use to interactively filter and explore various factors related to the house value of the San Francisco's neighborhoods.

Create a DataFrame showing the most expensive neighborhoods in San Francisco by year

# Fetch the data from all expensive neighborhoods per year.
# df_expensive_neighborhoods_per_year = df_costs[df_costs["neighborhood"].isin(df_expensive_neighborhoods["neighborhood"])]
# df_expensive_neighborhoods_per_year.head()
new = sfo_data.reset_index()
most_expensive = new.groupby('neighborhood').mean().sort_values(by = 'sale_price_sqr_foot', ascending = False).head(50)
most_expensive['sale_price_sqr_foot'] = np.floor(most_expensive['sale_price_sqr_foot'])
most_expensive['gross_rent'] = np.floor(most_expensive['gross_rent'])
most_expensive = most_expensive.reset_index()

most_expensive['year'] = round(most_expensive['year'])
most_expensive = pd.DataFrame(most_expensive)

Create a parallel coordinates plot and parallel categories plot of most expensive neighborhoods in San Francisco per year

# Parallel Categories Plot
px.parallel_categories(
    most_expensive,
    dimensions = ['neighborhood','year','housing_units','gross_rent'],
    color = 'sale_price_sqr_foot',
    labels={'neighborhood':'Neighborhood', 'sale_price_sqr_foot':'Sale Price/SqFt', 'housing_units':'Housing Units', 'gross_rent':'Gross Rent', 'year':'Year'},
    width = 1400,
    height = 800)

# Parallel Coordinates Plot
px.parallel_coordinates(
    most_expensive,
    dimensions = ['sale_price_sqr_foot', 'housing_units','gross_rent'],
    color = 'sale_price_sqr_foot',
    labels={'sale_price_sqr_foot':'Sale Price/SqFt', 'housing_units':'Housing Units', 'gross_rent':'Gross Rent'},
    width = 1400,
    height = 800)

Create a sunburst chart to conduct a costs analysis of most expensive neighborhoods in San Francisco per year

# Sunburst Plot
fig = px.sunburst(most_expensive, path = ['year','neighborhood',], values = 'sale_price_sqr_foot')
fig.show()

# Best I could do.